diff --git a/lynx/experements.ipynb b/lynx/experements.ipynb
new file mode 100644
index 0000000..6db7b2f
--- /dev/null
+++ b/lynx/experements.ipynb
@@ -0,0 +1,86 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 设备设置\n",
+    "* 单机 8  卡 A800 设备\n",
+    "* 两机 16 卡 A800 设备\n",
+    "\n",
+    "# 工作负载设置\n",
+    "* 四个模型（参数量不同、架构不同）\n",
+    "    - Llama 系列 2 个参数量不一样的\n",
+    "    - MoE 系列 2 个参数量不一样的\n",
+    "* 输入序列长度固定：512？？\n",
+    "    - batchsize 设置 / inputsize 设置 ？？\n",
+    "    - 输入大小固定好，输入大小取最大可行的批大小??\n",
+    "\n",
+    "# 基线设置\n",
+    "* XLA         : ALL\n",
+    "* SimpleFSDP  : 测试单一并行策略 FSDP \n",
+    "* Megatron-LM : 测试混合并行策略 EP, DP, TP???"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 统计指标\n",
+    "* 显存使用\n",
+    "* 吞吐率（samples/s）\n",
+    "* 编译时间\n",
+    "* iteration time\n",
+    "* MFU\n",
+    "* 通信比例，计算比例\n",
+    "* 通信/计算重叠比例\n",
+    "* 通信量\n",
+    "\n",
+    "# 测试目标\n",
+    "* Lynx能够在计算图上发现更多重叠机会，因此能够超过现在用于不同模型的不同并行策略的SOTA\n",
+    "\n",
+    "# 测试方法\n",
+    "* 单一并行方法，MoE和non-Moe测试 \n",
+    "  - lynx + SimpleFSDP\n",
+    "  - XLA + SimpleFSDP\n",
+    "  - SimpleFSDP\n",
+    "* 混合并行方法，non-MoE下测试 ?TP + ?DP，MoE下测试 ?TP + ?EP + ?DP\n",
+    "  - Lynx + Megatron-LM\n",
+    "  - XLA + Megatron-LM\n",
+    "  - Megatron-LM\n",
+    "\n",
+    "\n",
+    "# 测试结果：\n",
+    "总得来说Lynx能在不同配置中发现更多的重叠机会，以实现最大的重叠效率。\n",
+    "编译时间结果 - 额外开销时间不大\n",
+    "内存开销结果 - 不会引入太多的内存 cost\n",
+    "模型收敛 - 模型在训练时能够收敛"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 可扩展性\n",
+    "\n",
+    "# 测试目标\n",
+    "测试Lynx的可拓展性，节点变多时的性能增加\n",
+    "\n",
+    "# 测试方法：\n",
+    "选 FSDP 测试 LYNX，逐渐增加节点（basehsize 还是取最大可行的批大小）\n",
+    "\n",
+    "# 测试结果：\n",
+    "  - iteration time\n",
+    "  - MFU\n",
+    "  - 编译时间"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lynx/scalability.ipynb b/lynx/scalability.ipynb
new file mode 100644
index 0000000..559f5d2
--- /dev/null
+++ b/lynx/scalability.ipynb
@@ -0,0 +1,185 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
+    "plt.rcParams[\"font.size\"] = 16\n",
+    "\n",
+    "g_label_fontsize = 16\n",
+    "\n",
+    "colors = [\n",
+    "    \"#455D7A\",\n",
+    "    \"#233142\",\n",
+    "    \"#F95959\",\n",
+    "]\n",
+    "\n",
+    "edgecolors = [\n",
+    "    \"#FFFFFF\",\n",
+    "    \"#FFFFFF\",\n",
+    "    \"#FFFFFF\",\n",
+    "]\n",
+    "\n",
+    "hatches = [\"\\\\\\\\\", \"\", \"\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2100x840 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(\n",
+    "    figsize=(7, 14 / 5), ncols=1, nrows=1, constrained_layout=True, dpi=300\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels_name = [\n",
+    "    \"FSDP\",\n",
+    "    \"XLA\",\n",
+    "    \"DLRover-Lynx\"\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_data = [2, 4, 6, 8, 10, 12, 14, 16]\n",
+    "data_a = [53, 53, 53, 53, 53, 53, 53, 53]\n",
+    "data_b = [53, 53, 53, 53, 53, 53, 53, 53]\n",
+    "data_c = [65, 65, 65, 65, 65, 65, 65, 65]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "legend_labels = [\"FSDP\", \"XLA\", \"DLRover-Lynx\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(13.333333333333346, 0.5, 'MFU (%)')"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ax.plot(x_data, data_a, marker=\"o\", color=colors[0], label=legend_labels[0])\n",
+    "ax.plot(x_data, data_b, marker=\"x\", color=colors[1], label=legend_labels[1])\n",
+    "ax.plot(x_data, data_c, marker=\"o\", color=colors[2], label=legend_labels[2])\n",
+    "\n",
+    "ax.set_ylim(0, 100)\n",
+    "\n",
+    "ax.grid(True)\n",
+    "\n",
+    "ax.set_ylabel(\"MFU (%)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f57182abec0>"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ax.legend(\n",
+    "    ncol=3,\n",
+    "    loc=\"upper center\",\n",
+    "    frameon=True,\n",
+    "    shadow=False,\n",
+    "    fontsize=g_label_fontsize,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2100x840 with 1 Axes>"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig.savefig(\"scalability.pdf\", bbox_inches=\"tight\", dpi=1000)\n",
+    "fig"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lynx/scalability_compile_time.ipynb b/lynx/scalability_compile_time.ipynb
new file mode 100644
index 0000000..a90de86
--- /dev/null
+++ b/lynx/scalability_compile_time.ipynb
@@ -0,0 +1,185 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
+    "plt.rcParams[\"font.size\"] = 16\n",
+    "\n",
+    "g_label_fontsize = 16\n",
+    "\n",
+    "colors = [\n",
+    "    \"#455D7A\",\n",
+    "    \"#233142\",\n",
+    "    \"#F95959\",\n",
+    "]\n",
+    "\n",
+    "edgecolors = [\n",
+    "    \"#FFFFFF\",\n",
+    "    \"#FFFFFF\",\n",
+    "    \"#FFFFFF\",\n",
+    "]\n",
+    "\n",
+    "hatches = [\"\\\\\\\\\", \"\", \"\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2100x840 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(\n",
+    "    figsize=(7, 14 / 5), ncols=1, nrows=1, constrained_layout=True, dpi=300\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels_name = [\n",
+    "    \"FSDP\",\n",
+    "    \"XLA\",\n",
+    "    \"DLRover-Lynx\"\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_data = [2, 4, 6, 8, 10, 12, 14, 16]\n",
+    "data_a = [53, 53, 53, 53, 53, 53, 53, 53]\n",
+    "data_b = [53, 53, 53, 53, 53, 53, 53, 53]\n",
+    "data_c = [65, 65, 65, 65, 65, 65, 65, 65]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "legend_labels = [\"FSDP\", \"XLA\", \"DLRover-Lynx\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(13.333333333333346, 0.5, 'Time (s)')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ax.plot(x_data, data_a, marker=\"o\", color=colors[0], label=legend_labels[0])\n",
+    "ax.plot(x_data, data_b, marker=\"x\", color=colors[1], label=legend_labels[1])\n",
+    "ax.plot(x_data, data_c, marker=\"o\", color=colors[2], label=legend_labels[2])\n",
+    "\n",
+    "ax.set_ylim(0, 100)\n",
+    "\n",
+    "ax.grid(True)\n",
+    "\n",
+    "ax.set_ylabel(\"Time (s)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fd1de31f410>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ax.legend(\n",
+    "    ncol=3,\n",
+    "    loc=\"upper center\",\n",
+    "    frameon=True,\n",
+    "    shadow=False,\n",
+    "    fontsize=g_label_fontsize,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2100x840 with 1 Axes>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig.savefig(\"scalability_compile_time.pdf\", bbox_inches=\"tight\", dpi=1000)\n",
+    "fig"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}